Emergence of Cooperation Through Mutual Preference Revision

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


This paper proposes a method allowing an agent to perform in a socially fair way by considering other agents’ preferences. Such a balanced action selection process is based on declarative diagnosis, which enables the removal of contradictions arising as all agents’ preferences are confronted within the deciding agent. Agents can be negatively affected when some of their preferences are not respected for the sake of a global compromise. The set of preferences to be yielded by agents in order to remove all contradictions in a balanced way (i.e. the diagnosis that better manages how each agent is to be affected) is determined by minimising a cost function computed over application independent features. By respecting the resulting non-contradictory preferences set, the deciding agent acts cooperatively.


Preference revision multi-agent systems cooperative behaviour 


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  1. 1.
    Chomicki, J.: Preference formulas in relational queries. ACM Transactions on Database Systems 28, 427–466 (2003)CrossRefGoogle Scholar
  2. 2.
    Andreka, H., Ryan, M., Schobbens, P.Y.: Operators and laws for combining preference relations. Journal of Logic and Computation 12, 13–53 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Yager, R.R.: Fusion of multi-agent preference ordering. Fuzzy Sets and Systems 117, 1–12 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Rossi, F., Venable, K.B., Walsh, T.: mCP nets: representing and reasoning with preferences on multiple agents. In: AADEBUG 1993. LNCS, vol. 749, pp. 729–734. AAAI Press, Menlo Park (2004)Google Scholar
  5. 5.
    Doyle, J.: Prospects for preferences. Computational Intelligence 20, 111–136 (2004)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Dell’Acqua, P., Pereira, L.M.: Preference revision via declarative debugging. In: Bento, C., Cardoso, A., Dias, G. (eds.) EPIA 2005. LNCS, vol. 3808, pp. 18–28. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Pereira, L.M., Damásio, C., Alferes, J.J.: Debugging by diagnosing assumptions. In: Fritzson, P.A. (ed.) AADEBUG 1993. LNCS, vol. 749, pp. 58–74. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  8. 8.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Procs. of the 5th Int. Logic Programming Conf., MIT Press, Cambridge (1998)Google Scholar
  9. 9.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Gelder, A.V., Ross, K.A., Schlipf, J.S.: The well-founded semantics for general logic programs. J. ACM 38, 620–650 (1991)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.IntRoSys S.A.Portugal
  2. 2.Universidade Nova de Lisboa, Quinta da TorrePortugal

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